Director of Research (if dissertation) or Advisor (if thesis)
Srikant, Rayadurgam
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Pairwise comparison
Spectral clustering
Inference
Ranking
Abstract
We consider a pairwise comparisons model with n users and m items. Each user is shown a few pairs of items, and when a pair of items is shown to a user, he or she expresses a preference for one of the items based on a probabilistic model. The goal is to group users into clusters so that users within each cluster have similar preferences. We present an algorithm which clusters all users correctly with high probability using a number of pairwise comparisons which is within a polylog factor of a lower bound.
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